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Efficient privacy-preserving user authentication scheme with forward secrecy for industry 4.0

science China(Information sciences) 2022年第1期65卷 192-206页

作者： Chenyu WANG Ding WANG Guoai XU Debiao HE School of Cyber Security Beijing University of Posts and Telecommunications State Key Laboratory of Cryptology College of Cyber Science Nankai University Tianjin Key Laboratory of Network and Data Security Technology Nankai University School of Cyber Science and Engineering Wuhan University

Industry 4.0, which combines information technology, network and industrial production, is expected to have a tremendous impact on our daily lives. In such a complex and security-critical system with resource-constrained sensor nodes, the design of a secure user authentication scheme for preventing real-time data from unauthorized access is full of challenges, and the main crux lies in how to realize the important property of forward secrecy. Existing schemes either fail to achieve forward secrecy or achieve forward secrecy with high computation cost on sensor nodes. Besides, they often fail to conform to the development trend of industry 4.0 systems where a cloud center is necessary to help intelligent decision-making and alleviate computation and storage pressure. Therefore, in this paper, we propose an efficient privacy-preserving user authentication scheme with forward secrecy for industry 4.0, and formally prove its security in the random oracle model. Compared with previous schemes, it has three advantages:(1) all eleven state-of-the-art criteria are achieved;(2) its computation cost on sensor nodes is comparable to those insecure schemes that employ only symmetric cryptographic algorithms, and is superior to those that also use asymmetric cryptographic algorithms;(3) it takes the advantage of the computation and storage capabilities of the cloud center to achieve user anonymity and the resistance to offline dictionary attack without performing any asymmetric cryptographic algorithms on gateways. Our computation cost on gateways is the smallest among all state-of-the-art relevant schemes for comparison.

关键词： industry 4.0 wireless sensor networks password authentication forward secrecy offline dictionary attack

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A Client Selection Method Based on Loss Function Optimization for Federated Learning

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Computer Modeling in Engineering & sciences 2023年第10期137卷 1047-1064页

作者： Yan Zeng Siyuan Teng Tian Xiang Jilin Zhang Yuankai Mu Yongjian Ren Jian Wan School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Key Laboratory of Complex System Modeling and Simulation Ministry of EducationHangzhou310018China Zhejiang Engineering Research Center of Data Security Governance Hangzhou310018China Intelligent Robotics Research Center Zhejiang LabHangzhou311100China HDU-ITMO Joint Institute Hangzhou Dianzi UniversityHangzhou310018China

Federated learning is a distributedmachine learningmethod that can solve the increasingly serious problemof data islands and user data privacy,as it allows training data to be kept locally and not shared with other *** trains a globalmodel by aggregating locally-computedmodels of clients rather than their ***,the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global *** this problem,we focus on the client selection with federated learning,which can affect the convergence performance of the global model with the selected local *** propose FedChoice,a client selection method based on loss function optimization,to select appropriate local models to improve the convergence of the global *** firstly sets selected probability for clients with the value of loss function,and the client with high loss will be set higher selected probability,which can make them more likely to participate in ***,it introduces a local control vector and a global control vector to predict the local gradient direction and global gradient direction,respectively,and calculates the gradient correction vector to correct the gradient direction to reduce the cumulative deviationof the local gradient causedby *** experiments to verify the validity of FedChoice on CIFAR-10,CINIC-10,MNIST,EMNITS,and FEMNIST datasets,and the results show that the convergence of FedChoice is significantly improved,compared with FedAvg,FedProx,and FedNova.

关键词： Federated learning model aggregation Non-IID

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Secure YOLOv3-SPP: Edge-Cooperative Privacy-preserving Object Detection for Connected Autonomous Vehicles

Secure YOLOv3-SPP: Edge-Cooperative Privacy-preserving Objec...

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2022 International Conference on networking and network Applications, NaNA 2022

作者： Zhou, Yongjie Xiong, Jinbo Bi, Renwan Tian, Youliang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Fujian Provincial Key Laboratory of Network System Information Security College of Computer and Cyberspace Security Fujian Normal University FJ Fuzhou350117 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

The connected autonomous vehicles (CAVs) are a key component of intelligent transportation systems (ITS) where vehicles communicate with each other to exchange sensing data from on-board sensors (e.g., high-definition cameras and lidar). For the sake of the category and location privacy leakage of sensing images shared by CAVs and computational inefficiency of privacy-preserving object detection framework in edge computing environment, we propose a lightweight and privacy-preserving detection framework (PPDF) to support secure extraction, classification and detection of image features, and achieve the goal of data transmission and computing security under the collaborative detection process of edge nodes. Particularly, we design a secure clustering prediction protocol of object anchors, a secure object classification and regression protocol, secure object upsampling and secure feature fusion protocols. Finally, PPDF based on edge-cooperation was constructed and two non-collusive edge servers were used to perform PPDF. Theoretical analysis of correctness, security and complexity indicate that PPDF can not only realize correctness of object detection, but also let category and location privacy of images are protected effectively and have excellent accuracy. Actual performance evaluation shows that PPDF can achieve the same detection accuracy as original YOLOv3-SPP model. At the same time, compared with homomorphic encryption and multi-round iterative calculation schemes, PPDF has obvious advantages in terms of computational cost and communication overhead. © 2022 IEEE.

关键词： Autonomous vehicles

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Design and Implementation of Cyber Space Threat Detection System Based on User Behavioral Logs

Design and Implementation of Cyber Space Threat Detection Sy...

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Electronic Information Engineering and Computer Communication (EIECC), International Conference on

作者： Wenhao Wang Hao Li Peng Nie Tianjin Key Laboratory of Network and Data Security Technology College of Cyber Science Nankai University Tianjin China Science and Technology on Communication Networks Laboratory Shijiazhuang China College of Cyber Science Nankai University Tianjin China

In contemporary society, the role of networks in daily work and business activities is increasingly prominent. The enterprise intranet, serving as a crucial bridge for maintaining work stability, asset security, and business continuity, has garnered significant attention from both academia and industry in the field of intranet security detection. This study is based on the Log2Vec algorithm and involves the design and implementation of a Cyber Space Threat Detection System. The system, facilitated by the collaborative efforts of three modules— log ingestion, graph encoding, and security detection—effectively showcases user behavior, encodes logs into a heterogeneous graph, transforms the graph into vectors, and conducts cluster analysis to detect abnormal user behavior. Through in-depth research and systematic design, this study aims to provide enterprises with a more advanced and comprehensive intranet security detection solution to cope with evolving cybersecurity threats.

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Backdoor Defense via Deconfounded Representation Learning

Backdoor Defense via Deconfounded Representation Learning

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zaixi Zhang Qi Liu Zhicai Wang Zepu Lu Qingyong Hu Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei Anhui China University of Science and Technology of China Hong Kong University of Science and Technology

Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by injecting a few poisoned examples into the training dataset. While extensive efforts have been made to detect and remove backdoors from backdoored DNNs, it is still not clear whether a backdoor-free clean model can be directly obtained from poisoned datasets. In this paper, we first construct a causal graph to model the generation process of poisoned data and find that the backdoor attack acts as the confounder, which brings spurious associations between the input images and target labels, making the model predictions less reliable. Inspired by the causal understanding, we propose the Causality-inspired Backdoor Defense (CBD), to learn deconfounded representations for reliable classification. Specifically, a backdoored model is intentionally trained to capture the confounding effects. The other clean model dedicates to capturing the desired causal effects by minimizing the mutual information with the confounding representations from the backdoored model and employing a sample-wise re-weighting scheme. Extensive experiments on multiple benchmark datasets against 6 state-of-the-art attacks verify that our proposed defense method is effective in reducing backdoor threats while maintaining high accuracy in predicting benign samples. Further analysis shows that CBD can also resist potential adaptive attacks. The code is available at https://***/zaixizhang/CBD.

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Vertex-Critical (P5, chair)-Free Graphs

arXiv

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arXiv 2023年

作者： Huang, Shenwei Li, Zeyu College of Computer Science Nankai University Tianjin300350 China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300071 China

Given two graphs H1 and H2, a graph G is (H1, H2)-free if it contains no induced subgraph isomorphic to H1 or H2. A Pt is the path on t vertices. A chair is a P4 with an additional vertex adjacent to one of the middle vertices of the P4. A graph G is k-vertex-critical if G has chromatic number k but every proper induced subgraph of G has chromatic number less than k. In this paper, we prove that there are finitely many 5-vertex-critical (P5, chair)-free graphs. © 2023, CC BY.

关键词： Graphic methods

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ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus

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Computers in Biology and Medicine 2025年 186卷 109708-109708页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Ejiyi, Makuachukwu Bennedith Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Onyekwere, Favour Ezinne Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan 610059 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Pharmacy Department University of Nigeria Nsukka Enugu State Nsukka Nigeria School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

In image segmentation for medical image analysis, effective upsampling is crucial for recovering spatial information lost during downsampling. This challenge becomes more pronounced when dealing with diverse medical image modalities, which can significantly impact model performance. Plain and standard skip connections, widely used in most models, often fall short of maintaining high segmentation accuracy across different modalities, because essential spatial information transferred from the encoder to the decoder is lost. Inspired by these limitations, the Attention-Embedded Deep UNet (ATEDU-Net) is presented here, an innovative framework designed and tested on diverse medical image modalities, including X-ray, breast ultrasound, and retinal fundus images. ATEDU-Net features a unique architecture that combines progressive context refinement modules (PCRM) and global context modules (GCM) within a U-shaped network structure with Convgroup blocks which facilitate the integration of convolutional operations. This allows ATEDU-Net to autonomously capture rich spatial details and crucial contextual information from input images. The GCM captures essential contextual information for informed decision-making, while the PCRM enhances feature attention, resulting in precise and robust segmentation. The experimentation and analysis demonstrate that ATEDU-Net promises to be a powerful tool for medical professionals, aiding in the early detection of chest-related diseases, accurate localization of breast tumors, and early identification of eye diseases. This, in turn, contributes significantly to the formulation of optimal therapeutic strategies, enhancing patient care and outcomes. The versatility of ATEDU-Net is further highlighted by its ability to analyze medical images across various modalities, making it well-suited for the complex task of medical image analysis in diverse clinical scenarios. © 2025 Elsevier Ltd

关键词： Image segmentation

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Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet

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Computers in Biology and Medicine 2025年 185卷 109494-109494页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Yi, Ding Atwereboannah, Abena A. Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Bakanina Kissanga, Grace-Mercure Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering & Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center Chengdu University of Technology Sichuan Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Life Science University of Electronic Science and Technology of China Chengdu China

Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise of Computer-Aided Diagnosis Systems as reliable and cost-effective tools. Histopathology, renowned for its precision in cancer imaging, has become pivotal in the diagnostic landscape of breast, colon, and lung cancers. However, while deep learning models have been widely explored in this domain, they often face challenges in generalizing to diverse clinical settings and in efficiently capturing both local and global feature representations, particularly for multi-class tasks. This underscores the need for models that can reduce biases, improve diagnostic accuracy, and minimize error susceptibility in cancer classification tasks. To this end, we introduce ResoMergeNet (RMN), an advanced deep-learning model designed for both multi-class and binary cancer classification using histopathological images of breast, colon, and lung. ResoMergeNet integrates the Resboost mechanism which enhances feature representation, and the ConvmergeNet mechanism which optimizes feature extraction, leading to improved diagnostic accuracy. Comparative evaluations against state-of-the-art models show ResoMergeNet's superior performance. Validated on the LC-25000 and BreakHis (400× and 40× magnifications) datasets, ResoMergeNet demonstrates outstanding performance, achieving perfect scores of 100 % in accuracy, sensitivity, precision, and F1 score for binary classification. For multi-class classification with five classes from the LC25000 dataset, it maintains an impressive 99.96 % across all performance metrics. When applied to the BreakHis dataset, ResoMergeNet achieved 99.87 % accuracy, 99.75 % sensitivity, 99.78 % precision, and 99.77 % F1 score at 400× magnification. At 40× magnification, it still delivered robust results with 98.85 % accuracy,

关键词： Lung cancer

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A Lightweight Collaborative Target Recognition Method for Unmanned Aerial Vehicle Cluster

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IEEE Internet of Things Journal 2025年

作者： Cao, Mingsheng Yin, Yiyang Zhang, Li Li, Weizhuang Liu, Ziqiang Zhu, Ruizheng Zhao, Yang University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering Chengdu611731 China University of Electronic Science and Technology of China ChengDu Research Institute Chengdu610054 China Nanjing University of Aeronautics and Astronautics School of Aerospace Engineering Nanjing210001 China

This paper aims to explore a reliable target recognition technique for unmanned aerial vehicle (UAV) clusters, and proposes a lightweight collaborative target recognition methods based on multiple viewpoints. In the proposed method, the UAVs are divided into the leaf node UAVs and the head node UAVs. Leaf node UAVs are used for multi-view image acquisition and image feature extraction by utilizing a lightweight feature extraction model. The head node UAVs realize efficient feature fusion from the collected image features by using graph convolutional network and graph coarsening techniques. Based on the above lightweight technologies, the proposed method can realize the efficient and accurate target recognition and reduce the demand for limited computing resources and communication resources in UAV clusters. Experimental results show that, compared with existing multi-view object recognition method, the proposed method has less computing cost and communication overhead while ensuring reliable recognition accuracy. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Computer aided diagnosis of diabetic retinopathy based on multi-view joint learning

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Computers in Biology and Medicine 2024年 174卷 108428-108428页

作者： Xu, Xuebin Liu, Dehua Huang, Guohua Wang, Muyu Lei, Meng Jia, Yang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China Weinan Central Hospital Shaanxi Xi'an714099 China

Diabetic retinopathy (DR) is a kind of ocular complication of diabetes, and its degree grade is an essential basis for early diagnosis of patients. Manual diagnosis is a long and expensive process with a specific risk of misdiagnosis. Computer-aided diagnosis can provide more accurate and practical treatment recommendations. In this paper, we propose a multi-view joint learning DR diagnostic model called RT2Net, which integrates the global features of fundus images and the local detailed features of vascular images to reduce the limitations of single fundus image learning. Firstly, the original image is preprocessed using operations such as contrast-limited adaptive histogram equalization, and the vascular structure of the extracted DR image is segmented. Then, the vascular image and fundus image are input into two branch networks of RT2Net for feature extraction, respectively, and the feature fusion module adaptively fuses the feature vectors’ output from the branch networks. Finally, the optimized classification model is used to identify the five categories of DR. This paper conducts extensive experiments on the public datasets EyePACS and APTOS 2019 to demonstrate the method's effectiveness. The accuracy of RT2Net on the two datasets reaches 88.2% and 85.4%, and the area under the receiver operating characteristic curve (AUC) is 0.98 and 0.96, respectively. The excellent classification ability of RT2Net for DR can significantly help patients detect and treat lesions early and provide doctors with a more reliable diagnosis basis, which has significant clinical value for diagnosing DR. © 2024 The Authors

关键词： Computer aided diagnosis

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